Texture Analysis

Texture analysis focuses on extracting meaningful information from the spatial arrangement of image intensities, aiming to classify, segment, or otherwise understand image content based on its textural properties. Current research emphasizes the application of deep learning architectures, such as convolutional neural networks and vision transformers, often combined with traditional methods like Gray Level Co-occurrence Matrices (GLCM) and Local Binary Patterns (LBP), to improve feature extraction and classification accuracy. This field is crucial for diverse applications, ranging from medical image analysis for disease diagnosis to robotic manipulation and security applications like forgery detection, where accurate and efficient texture analysis is essential for reliable performance.